Digitisation can unlock renewables’ vast potential

The global energy sector is transforming. Fast. The emerging energy ecosystem is decentralised, decarbonised and digitised – refashioning our energy world as we knew it. For those who seek to accelerate this transformation as an environmental imperative, economics is our powerfully ally.

To be clear, the urgency of embracing renewables grows each day. CO 2 in the atmosphere increased by nearly 2 parts per million in the last year alone. The economic impacts of this atmospheric heating are decidedly chilling to the economy. The Federal Reserve Bank of Richmond recently asserted that “rising temperatures could reduce US economic growth by up to one-third over the next century.” And of the World Economic Forum’s 2017-18 top five risks facing humanity globally, three involve weather and climate.

Thankfully, many of the changes we are seeing are driven by economics. In 2016, for the first time in history, investment in electricity ($718bn) surpassed investment in oil and gas ($708bn), according to the International Energy Agency’s World Energy Investment report.

And by the same year, almost half of the world’s Fortune 500 companies were investing in greenhouse gas reductions, sustainability and renewable energy initiatives. Notable? Yes, but the bigger story here is not just the economic viability of large-scale wind and solar energy, but that even at this early stage, digitization is serving as a powerful economic lever. Low-cost hardware, cloud software and big data analytics can collectively reduce the levelised cost of energy (LCOE) delivered. Here’s how:

First, secure situational awareness. Our infrastructure is increasingly instrumented, inter-connected and intelligent, producing a veritable torrent of grid data estimated to top 150 petabytes – for a sense of scale, that is 150 followed by 15 zeros – annually by 2020. One could get lost in this tsunami of data.

The first step in an analytics journey is to paint a picture through “descriptive analytics”that provide holistic situational awareness of equipment and grids. Intelligent dashboarding tools can provide key performance indicators, alerts, real-time status, and revealing insights through relatively straightforward data manipulation. And if the data challenge seems daunting, the solution is not a futile quest for data perfection, but rather automated data curation techniques.

Second, invest in preventive healthcare for expensive assets. Asset instrumentation is driving the evolution of maintenance techniques that maximize returns. Time-based maintenance is rapidly giving way to condition- and risk-based maintenance by mining instrumentation data for patterns and anomalies. These techniques predict precise probabilities of failure using “prescriptive analytics” – taking advantage of the equivalent of a blood test or cardiogram every few minutes to perform preventive health care on expensive energy assets.

Third, predict the future. Renewable energy is increasingly bid or auctioned into energy markets rather than being favored with beneficial Power Purchase Agreements (PPAs). “Predictive analytics”can be used to combine hyper-local, hyper-accurate specialised weather prediction with advanced machine-learning to produce precise forecasts of energy production from wind and solar farms with sufficient lead time to enable optimal market trading and avoid mis-prediction penalties

Fourth, reduce battery costs by exploiting flexibility. With the growth of electric vehicles and heat pumps, our electricity demand profile is increasingly flexible and can be shifted in time. Dealing with variability by exploiting this flexibility is vastly more cost effective than utility-scale battery storage, which still has a way to go on its cost curve.

“Real-time analytics”can be used to model and exploit flexibility by an array of techniques such as Time Of Use (or TOU) billing, automated thermostat controls, including “Rush Hour Rewards,” flexible vehicle charging and Transactive Energy Management (TEM).

Fifth, promote a broader vision. Renewables’ success requires effective integration of increasingly higher fractions of renewable energy – a view beyond the fence of the wind/solar farm and deep into the grid that is distributing and using that energy. That necessitates comprehensive modeling of grid energy inflows and outflows, kept in harmony by digital intelligence, to enable optimal use of battery storage, energy balancing, peak load prediction and reduction, reliable power quality, congestion mitigation and minimisation of curtailment.

Ultimately, it’s all about the base. These capabilities imply an integrated set of analytics software tools, built on a new kind of energy analytics platform. A platform that’s secure, brings to bear the state-of-the-art in data science and machine learning, and includes stochastic optimisation techniques to deal with uncertainty. A platform that provides common data models and data curation to deal with missing, inconsistent or dirty data. A platform that is energy-specialised and spans the ecosystem from generation and energy markets to transmission and distribution all the way to the grid edge.

In short, a grand conductor of the symphonic energy orchestra, the brains behind an operating system of energy.

I am an optimist. We can marry data science with sustainable energy science for a cleaner, more reliable and affordable energy future. Together we must embrace this digitised opportunity to finally unlock the vast potential of renewable energy, in order to overcome the pressing challenge of our warming world.

Digitisation can unlock renewables’ vast potential

The global energy sector is transforming. Fast. The emerging energy ecosystem is decentralised, decarbonised and digitised – refashioning our energy world as we knew it. For those who seek to accelerate this transformation as an environmental imperative, economics is our powerfully ally.

To be clear, the urgency of embracing renewables grows each day. CO 2 in the atmosphere increased by nearly 2 parts per million in the last year alone. The economic impacts of this atmospheric heating are decidedly chilling to the economy. The Federal Reserve Bank of Richmond recently asserted that “rising temperatures could reduce US economic growth by up to one-third over the next century.” And of the World Economic Forum’s 2017-18 top five risks facing humanity globally, three involve weather and climate.

Thankfully, many of the changes we are seeing are driven by economics. In 2016, for the first time in history, investment in electricity ($718bn) surpassed investment in oil and gas ($708bn), according to the International Energy Agency’s World Energy Investment report.

And by the same year, almost half of the world’s Fortune 500 companies were investing in greenhouse gas reductions, sustainability and renewable energy initiatives. Notable? Yes, but the bigger story here is not just the economic viability of large-scale wind and solar energy, but that even at this early stage, digitization is serving as a powerful economic lever. Low-cost hardware, cloud software and big data analytics can collectively reduce the levelised cost of energy (LCOE) delivered. Here’s how:

First, secure situational awareness. Our infrastructure is increasingly instrumented, inter-connected and intelligent, producing a veritable torrent of grid data estimated to top 150 petabytes – for a sense of scale, that is 150 followed by 15 zeros – annually by 2020. One could get lost in this tsunami of data.

The first step in an analytics journey is to paint a picture through “descriptive analytics”that provide holistic situational awareness of equipment and grids. Intelligent dashboarding tools can provide key performance indicators, alerts, real-time status, and revealing insights through relatively straightforward data manipulation. And if the data challenge seems daunting, the solution is not a futile quest for data perfection, but rather automated data curation techniques.

Second, invest in preventive healthcare for expensive assets. Asset instrumentation is driving the evolution of maintenance techniques that maximize returns. Time-based maintenance is rapidly giving way to condition- and risk-based maintenance by mining instrumentation data for patterns and anomalies. These techniques predict precise probabilities of failure using “prescriptive analytics” – taking advantage of the equivalent of a blood test or cardiogram every few minutes to perform preventive health care on expensive energy assets.

Third, predict the future. Renewable energy is increasingly bid or auctioned into energy markets rather than being favored with beneficial Power Purchase Agreements (PPAs). “Predictive analytics”can be used to combine hyper-local, hyper-accurate specialised weather prediction with advanced machine-learning to produce precise forecasts of energy production from wind and solar farms with sufficient lead time to enable optimal market trading and avoid mis-prediction penalties

Fourth, reduce battery costs by exploiting flexibility. With the growth of electric vehicles and heat pumps, our electricity demand profile is increasingly flexible and can be shifted in time. Dealing with variability by exploiting this flexibility is vastly more cost effective than utility-scale battery storage, which still has a way to go on its cost curve.

“Real-time analytics”can be used to model and exploit flexibility by an array of techniques such as Time Of Use (or TOU) billing, automated thermostat controls, including “Rush Hour Rewards,” flexible vehicle charging and Transactive Energy Management (TEM).

Fifth, promote a broader vision. Renewables’ success requires effective integration of increasingly higher fractions of renewable energy – a view beyond the fence of the wind/solar farm and deep into the grid that is distributing and using that energy. That necessitates comprehensive modeling of grid energy inflows and outflows, kept in harmony by digital intelligence, to enable optimal use of battery storage, energy balancing, peak load prediction and reduction, reliable power quality, congestion mitigation and minimisation of curtailment.

Ultimately, it’s all about the base. These capabilities imply an integrated set of analytics software tools, built on a new kind of energy analytics platform. A platform that’s secure, brings to bear the state-of-the-art in data science and machine learning, and includes stochastic optimisation techniques to deal with uncertainty. A platform that provides common data models and data curation to deal with missing, inconsistent or dirty data. A platform that is energy-specialised and spans the ecosystem from generation and energy markets to transmission and distribution all the way to the grid edge.

In short, a grand conductor of the symphonic energy orchestra, the brains behind an operating system of energy.

I am an optimist. We can marry data science with sustainable energy science for a cleaner, more reliable and affordable energy future. Together we must embrace this digitised opportunity to finally unlock the vast potential of renewable energy, in order to overcome the pressing challenge of our warming world.

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